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气候适应型作物:整合人工智能、多组学和先进表型分析以应对全球农业和社会挑战

Climate-Resilient Crops: Integrating AI, Multi-Omics, and Advanced Phenotyping to Address Global Agricultural and Societal Challenges.

作者信息

Thingujam Doni, Gouli Sandeep, Cooray Sachin Promodh, Chandran Katie Busch, Givens Seth Bradley, Gandhimeyyan Renganathan Vellaichamy, Tan Zhengzhi, Wang Yiqing, Patam Keerthi, Greer Sydney A, Acharya Ranju, Moseley David Octor, Osman Nesma, Zhang Xin, Brooker Megan E, Tagert Mary Love, Schafer Mark J, Jeong Changyoon, Hoffseth Kevin Flynn, Bheemanahalli Raju, Wyss J Michael, Wijewardane Nuwan Kumara, Ham Jong Hyun, Mukhtar M Shahid

机构信息

Department of Biological Sciences, Clemson University, 132 Long Hall, Clemson, SC 29634, USA.

Department of Plant Pathology and Crop Physiology, Louisiana State University Agricultural Center, Baton Rouge, LA 70803, USA.

出版信息

Plants (Basel). 2025 Aug 29;14(17):2699. doi: 10.3390/plants14172699.

Abstract

Drought and excess ambient temperature intensify abiotic and biotic stresses on agriculture, threatening food security and economic stability. The development of climate-resilient crops is crucial for sustainable, efficient farming. This review highlights the role of multi-omics encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics in identifying genetic pathways for stress resilience. Advanced phenomics, using drones and hyperspectral imaging, can accelerate breeding programs by enabling high-throughput trait monitoring. Artificial intelligence (AI) and machine learning (ML) enhance these efforts by analyzing large-scale omics and phenotypic data, predicting stress tolerance traits, and optimizing breeding strategies. Additionally, plant-associated microbiomes contribute to stress tolerance and soil health through bioinoculants and synthetic microbial communities. Beyond agriculture, these advancements have broad societal, economic, and educational impacts. Climate-resilient crops can enhance food security, reduce hunger, and support vulnerable regions. AI-driven tools and precision agriculture empower farmers, improving livelihoods and equitable technology access. Educating teachers, students, and future generations fosters awareness and equips them to address climate challenges. Economically, these innovations reduce financial risks, stabilize markets, and promote long-term agricultural sustainability. These cutting-edge approaches can transform agriculture by integrating AI, multi-omics, and advanced phenotyping, ensuring a resilient and sustainable global food system amid climate change.

摘要

干旱和环境温度过高加剧了农业面临的非生物和生物胁迫,威胁着粮食安全和经济稳定。培育适应气候变化的作物对于可持续、高效农业至关重要。本综述强调了涵盖基因组学、转录组学、蛋白质组学、代谢组学和表观基因组学的多组学在识别抗逆遗传途径中的作用。利用无人机和高光谱成像的先进表型组学,能够通过高通量性状监测加速育种计划。人工智能(AI)和机器学习(ML)通过分析大规模组学和表型数据、预测抗逆性状以及优化育种策略,加强了这些工作。此外,与植物相关的微生物群落通过生物菌剂和合成微生物群落有助于提高植物的抗逆性和土壤健康。除农业领域外,这些进展还具有广泛的社会、经济和教育影响。适应气候变化的作物能够增强粮食安全、减少饥饿并支持脆弱地区。人工智能驱动的工具和精准农业使农民受益,改善他们的生计并提供公平的技术获取机会。对教师、学生和后代进行教育能够提高认识,并使他们有能力应对气候挑战。在经济方面,这些创新降低了金融风险、稳定了市场并促进了长期农业可持续性。这些前沿方法可以通过整合人工智能、多组学和先进表型分析来变革农业,在气候变化背景下确保全球粮食系统具有抗逆性和可持续性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0be6/12429994/57bdd5428ec6/plants-14-02699-g001.jpg

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